智能自适应边缘系统：探索与挑战

王旭; 陈南希; 张柔佳

doi:10.11959/j.issn.2096-3750.2021.00210

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智能自适应边缘系统：探索与挑战

专题：物联网边缘智能与雾计算 | 更新时间：2024-06-05

- 智能自适应边缘系统：探索与挑战
- Intelligent adaptive edge systems：exploration and open issues
- 物联网学报 2021年5卷第1期页码：1-10
- 作者机构：
  
  1. 中国科学院上海微系统与信息技术研究所，上海 200050
  2. 中国科学院大学，北京 100049
  3. 华为技术有限公司上海研究所，上海 201206
- 作者简介：
  
  [ "王旭（1986- ），男，博士，中国科学院上海微系统与信息技术研究所副研究员，主要研究方向为无线通信、深度学习等" ]
  [ "陈南希（1989- ），女，博士，中国科学院上海微系统与信息技术研究所副研究员、云脑课题组组长，主要研究方向为泛在智能、物联网、深度学习等" ]
  [ "张柔佳（1995- ），女，华为技术有限公司上海研究所测试工程师，主要研究方向为通信和信息技术、服务计算等" ]
- 基金信息：
  
  上海市青年科技英才扬帆计划资助项目;Shanghai Sailing Program(19YF1455900)
- DOI：10.11959/j.issn.2096-3750.2021.00210
  中图分类号： TP393.0
- 纸质出版日期：2021-03-30，
  
  网络出版日期：2021-03，
- 稿件说明：
移动端阅览
王旭, 陈南希, 张柔佳. 智能自适应边缘系统：探索与挑战[J]. 物联网学报, 2021,5(1):1-10.

XU WANG, NANXI CHEN, ROUJIA ZHANG. Intelligent adaptive edge systems：exploration and open issues. [J]. Chinese journal on internet of things, 2021, 5(1): 1-10.
王旭, 陈南希, 张柔佳. 智能自适应边缘系统：探索与挑战[J]. 物联网学报, 2021,5(1):1-10. DOI： 10.11959/j.issn.2096-3750.2021.00210.

XU WANG, NANXI CHEN, ROUJIA ZHANG. Intelligent adaptive edge systems：exploration and open issues. [J]. Chinese journal on internet of things, 2021, 5(1): 1-10. DOI： 10.11959/j.issn.2096-3750.2021.00210.

摘要

边缘智能已成为新一代物联网的发展趋势。边缘计算设备地理分布广，设备种类多，服务多样化，时延敏感，终端具备移动性。因此，边缘系统需要提供灵活多样的、可重构可扩充的服务。通过将自适应思想融入边缘计算，首先探索了智能自适应边缘系统应用需求，分析并总结了现有自适应边缘系统基础框架，并将深度学习、强化学习等人工智能技术应用于自适应边缘系统。然后，介绍了如何在特定的应用领域设计专门的智能算法。最后，探讨了该领域的发展潜力以及未来面临的挑战。

Abstract

Edge intelligence has emerged as a promising trend of the new generation of Internet of things.Edge computing devices are widely distributed

with various diverse end devices and services

delay sensitive

and serve mobile terminals.Therefore

the edge system needs to provide flexible

diverse

reconfigurable and scalable services.From the application fields of adaptive edge computing

the application requirements of intelligent adaptive edge systems were explored

the existing adaptive edge systems and their basic framework were analyzed and summarized

and the application of artificial intelligence technologies was discussed

such as deep learning and reinforcement learning.Then

how to design a special intelligent algorithm in specific application fields was introduced.Finally

the research status and future challenges in this field were discussed.

关键词

边缘计算自适应系统物联网MAPE-K循环控制

Keywords

edge computingadaptive systemInternet of thingsMAPE-K control loop

references

SASAKI K, SUZUKI N, MAKIDO S ,et al. Vehicle control system coordinated between cloud and mobile edge computing[C]// Proceedings of 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). Piscataway:IEEE Press, 2016: 1122-1127.

BARTHÉLEMY J, VERSTAEVEL N, FOREHEAD H ,et al. Edge-computing video analytics for real-time traffic monitoring in a smart city[J]. Sensors, 2019,19(9): 2048.

OKAY F Y, OZDEMIR S . A fog computing based smart grid model[C]// Proceedings of 2016 International Symposium on Networks,Computers and Communications (ISNCC). Piscataway:IEEE Press, 2016: 1-6.

VARGHESE B, WANG N, BARBHUIYA S ,et al. Challenges and opportunities in edge computing[C]// Proceedings of 2016 IEEE International Conference on Smart Cloud (SmartCloud). Piscataway:IEEE Press, 2016: 20-26.

BIBRI S E, KROGSTIE J . Smart sustainable cities of the future:an extensive interdisciplinary literature review[J]. Sustainable Cities and Society, 2017,31: 183-212.

YANG Y . Multi-tier computing networks for intelligent IoT[J]. Nature Electronics, 2019,2(1): 4-5.

YANG Y, LUO X, CHU X ,et al. Fog-enabled intelligent IoT systems[M]. Berlin: Springer, 2019.

SHI W S, DUSTDAR S . The promise of edge computing[J]. Computer, 2016,49(5): 78-81.

TRAN T X, POMPILI D . Adaptive bitrate video caching and processing in mobile-edge computing networks[J]. IEEE Transactions on Mobile Computing, 2019,18(9): 1965-1978.

CAO N Y, NASIR S B, SEN S ,et al. Self-optimizing IoT wireless video sensor node with in situ data analytics and context-driven energy-aware real-time adaptation[J]. IEEE Transactions on Circuits and Systems I:Regular Papers, 2017,64(9): 2470-2480.

WANG K, SHAO Y, XIE L ,et al. Adaptive and fault-tolerant data processing in healthcare IoT based on fog computing[J]. IEEE Transactions on Network Science and Engineering, 2020,7(1): 263-273.

LIANG C C, HE Y, YU F R ,et al. Enhancing video rate adaptation with mobile edge computing and caching in software-defined mobile networks[J]. IEEE Transactions on Wireless Communications, 2018,17(10): 7013-7026.

WANG L, JIAO L, LI J ,et al. Online resource allocation for arbitrary user mobility in distributed edge clouds[C]// Proceedings of 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). Piscataway:IEEE Press, 2017: 1281-1290.

CASADEI R, PIANINI D, VIROLI M ,et al. Self-organising coordination regions:a pattern for edge computing[C]// Proceedings of International Conference on Coordination Languages and Models.[S.l.:s.n.], 2019: 182-199.

ZHANG T H, JIN J, ZHENG X ,et al. Rate-adaptive fog service platform for heterogeneous IoT applications[J]. IEEE Internet of Things Journal, 2020,7(1): 176-188.

WEN Z Y, YANG R Y, GARRAGHAN P ,et al. Fog orchestration for Internet of things services[J]. IEEE Internet Computing, 2017,21(2): 16-24.

XU J, CHEN L X, REN S L . Online learning for offloading and autoscaling in energy harvesting mobile edge computing[J]. IEEE Transactions on Cognitive Communications and Networking, 2017,3(3): 361-373.

SEIGER R, HUBER S, HEISIG P ,et al. Toward a framework for self-adaptive workflows in cyber-physical systems[J]. Software ＆Systems Modeling, 2019,18(2): 1117-1134.

LIN B, ZHU F N, ZHANG J S ,et al. A time-driven data placement strategy for a scientific workflow combining edge computing and cloud computing[J]. IEEE Transactions on Industrial Informatics, 2019,15(7): 4254-4265.

SODHRO A H, PIRBHULAL S, ALBUQUERQUE V H C D . Artificial intelligence-driven mechanism for edge computing-based industrial applications[J]. IEEE Transactions on Industrial Informatics, 2019,15(7): 4235-4243.

CAO K, ZHOU J L, XU G ,et al. Exploring renewable-adaptive computation offloading for hierarchical QoS optimization in fog computing[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020,39(10): 2095-2108.

ZHAO T Q, ZHANG W, ZHAO H Y ,et al. A reinforcement learning-based framework for the generation and evolution of adaptation rules[C]// Proceedings of 2017 IEEE International Conference on Autonomic Computing (ICAC). Piscataway:IEEE Press, 2017: 103-112.

TANG Z Q, ZHOU X J, ZHANG F M ,et al. Migration modeling and learning algorithms for containers in fog computing[J]. IEEE Transactions on Services Computing, 2019,12(5): 712-725.

SEIGER R, HUBER S, HEISIG P ,et al. Enabling self-adaptive workflows for cyber-physical systems[C]// Proceedings of Enterprise,Business-Process and Information Systems Modeling.[S.l.:s.n.], 2016.

FERRÁNDEZ-PASTOR F J, MORA H， JIMENO-MORENILLA A ,et al. Deployment of IoT edge and fog computing technologies to develop smart building services[J]. Sustainability, 2018,10(11): 1-23.

GATOUILLAT A, BADR Y, MASSOT B . QoS-driven self-adaptation for critical IoT-based systems[C]// Proceedings of International Conference on Service-Oriented Computing.[S.l.:s.n.], 2017: 93-105.

KIT M, GEROSTATHOPOULOS I, BURES T ,et al. An architecture framework for experimentations with self-adaptive cyber-physical systems[C]// Proceedings of 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems.[S.l.:s.n.], 2015: 93-96.

WEYNS D, RAMACHANDRAN G, SINGH R . Self-managing Internet of things[C]// Proceedings of International Conference on Current Trends in Theory and Practice of Informatics.[S.l.:s.n.], 2018: 1-18.

TAHERIZADEH S, JONES A, TAYLOR I ,et al. Monitoring self-adaptive applications within edge computing frameworks:a state-of-the-art review[J]. Journal of Systems and Software, 2018,136: 19-38.

ZHANG L, ALHARBE N, ATKINS A . An IoT application for inventory management with a self-adaptive decision model[C]// Proceedings of IEEE International Conference on Internet of Things. Piscataway:IEEE Press, 2016: 317-322.

RAVINDRA P, KHOCHARE A, REDDY S ,et al. ECHO:an adaptive orchestration platform for hybrid dataflows across cloud and edge[C]// Proceedings of international Conference on Service-Oriented Computing.[S.l.:s.n.], 2017: 395-410.

ZOLOTUKHIN M, HÄMÄLÄINEN T, KOKKONEN T ,et al. Increasing web service availability by detecting application-layer DDoS attacks in encrypted traffic[C]// Proceedings of 2016 23rd International Conference on Telecommunications (ICT). Piscataway:IEEE Press, 2016: 1-6.

SONG W, YIN H, LIU C ,et al. DeepMem:learning graph neural network models for fast and robust memory forensic analysis[C]// Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. New York:ACM Press, 2018: 606-618.

CAPORUSCIO M, ANGELO M D, GRASSI V ,et al. Reinforcement learning techniques for decentralized self-adaptive service assembly[C]// Proceedings of 5th European Conference on Service-Oriented and Cloud Computing (ESOCC).[S.l.:s.n.], 2016: 53-68.

MU T Y, AL-FUQAHA A, SHUAIB K ,et al. SDN flow entry management using reinforcement learning[J]. ACM Transactions on Autonomous and Adaptive Systems, 2018,13(2): 1-23.

RAHMAN W U, HONG C S, HUH E N . Edge computing assisted joint quality adaptation for mobile video streaming[J]. IEEE Access, 2019(7): 129082-129094.

SILVA R A C D, FONSECA N L S D . Resource allocation mechanism for a fog-cloud infrastructure[C]// Proceedings of 2018 IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2018: 1-6.

XIAO Y H, JIA Y Z, LIU C C ,et al. Edge computing security:state of the art andchallenges[J]. Proceedings of the IEEE, 2019,107(8): 1608-1631.

WEYNS D . Handbook of software engineering[M]. Berlin: Springer, 2019.

LI Q, ZHANG Y M, LI Y Y ,et al. Capacity-aware edge caching in fog computing networks[J]. IEEE Transactions on Vehicular Technology, 2020,69(8): 9244-9248.

XIAO Y, KRUNZ M . Dynamic network slicing for scalable fog computing systems with energy harvesting[J]. IEEE Journal on Selected Areas in Communications, 2018,36(12): 2640-2654.

CHEN X, JIAO L, LI W Z ,et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM Transactions on Networking, 2016,24(5): 2795-2808.

LIN R P, ZHOU Z J, LUO S.et al . Distributed optimization for computation offloading in edge computing[J]. IEEE Transactions on Wireless Communications, 2020,19(12): 8179-8194.

CHEN X, SHI Q, YANG L ,et al. Thrifty edge:resource-efficient edge computing for intelligent IoT applications[J]. IEEE Network, 2018,32(1): 61-65.

D’ANGELO M, . Decentralized self-adaptive computing at the edge[C]// Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems.[S.l.:s.n.], 2018: 144-148.

LIU Q Y, WEI Y K, LENG S P ,et al. Task scheduling in fog enabled Internet of things for smart cities[C]// Proceedings of IEEE 17th International Conference on Communication Technology (ICCT). Piscataway:IEEE Press, 2017: 975-980.

YIGITOGLU E, MOHAMED M, LIU L ,et al. Foggy:a framework for continuous automated IoT application deployment in fog computing[C]// Proceedings of 2017 IEEE International Conference on AI ＆Mobile Services (AIMS). Piscataway:IEEE Press, 2017: 38-45.

MINH Q T, NGUYEN D T, VAN L A ,et al. Toward service placement on fog computing landscape[C]// Proceedings of 2017 4th NAFOSTED Conference on Information and Computer Science. Piscataway:IEEE Press, 2017: 291-296.

JUTILA M . An adaptive edge router enabling Internet of things[J]. IEEE Internet of Things Journal, 2016,3(6): 1061-1069.

CHEN L X, ZHOU P, GAO L ,et al. Adaptive fog configuration for the industrial Internet of things[J]. IEEE Transactions on Industrial Informatics, 2018,14(10): 4656-4664.

ORSINI G, BADE D, LAMERSDORF W . Cloud aware:a context-adaptive middleware for mobile edge and cloud computing applications[C]// Proceedings of 2016 IEEE 1st International Workshops on Foundations ＆ Applications of Self Systems. Piscataway:IEEE Press, 2016: 216-221.

CHEN N X, YANG Y, ZHANG T ,et al. Fog as a service technology[J]. IEEE Communications Magazine, 2018,56(11): 95-101.

NGUYEN T D, KIM Y, KIM D H ,et al. A proposal of autonomic edge cloud platform with CCN-based service routing protocol[C]// Proceedings of 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). Piscataway:IEEE Press, 2018: 802-809.

DANESHFAR N, PAPPAS N, POLISHCHUK V ,et al. Service allocation in a mobile fog infrastructure under availability and QoS constraints[C]// Proceedings of 2018 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE Press, 2018: 1-6.

HSIEH Y C, HONG H J, TSAI P H ,et al. Managed edge computing on Internet-of-things devices for smart city applications[C]// Proceedings of NOMS 2018 IEEE/IFIP Network Operations and Management Symposium. Piscataway:IEEE Press, 2018: 1-2.

GEROSTATHOPOULOS I, BURES T, HNETYNKA P ,et al. Self-adaptation in software-intensive cyber-physical systems:from system goals to architecture configurations[J]. Journal of Systems and Software, 2016: 1-20.

XIAO Y, SHI G M, LI Y Y ,et al. Toward self-learning edge intelligence in 6G[J]. IEEE Communications Magazine, 2020,58(12): 34-40.

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